{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"#  &#x1F4D1; **作业10：对抗攻击(Adversarial Attack)**\n\nPPT: https://reurl.cc/7DDxnD\n\n联系邮箱: ntu-ml-2022spring-ta@googlegroups.com","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"## 环境设设置 & 数据下载\n\n我们使用 [pytorchcv](https://pypi.org/project/pytorchcv/) 获取 `CIFAR-10` 预训练模型   \n所以我们需要先建立环境。我们还需要下载我们想要攻击的数据（200张图像）。","metadata":{}},{"cell_type":"code","source":"# 设置环境\n!pip install pytorchcv\n!pip install imgaug\n\n# 下载数据\n!wget https://github.com/DanielLin94144/ML-attack-dataset/files/8167812/data.zip\n\n# 解压\n!unzip ./data.zip\n!rm ./data.zip","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:23:23.105838Z","iopub.execute_input":"2023-05-10T13:23:23.106566Z","iopub.status.idle":"2023-05-10T13:23:54.957352Z","shell.execute_reply.started":"2023-05-10T13:23:23.106535Z","shell.execute_reply":"2023-05-10T13:23:54.955959Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting pytorchcv\n  Downloading pytorchcv-0.0.67-py2.py3-none-any.whl (532 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m532.4/532.4 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from pytorchcv) (1.21.6)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from pytorchcv) (2.28.2)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->pytorchcv) (2.1.1)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->pytorchcv) (2022.12.7)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->pytorchcv) (3.4)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->pytorchcv) (1.26.14)\nInstalling collected packages: pytorchcv\nSuccessfully installed pytorchcv-0.0.67\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mRequirement already satisfied: imgaug in /opt/conda/lib/python3.7/site-packages (0.4.0)\nRequirement already satisfied: scikit-image>=0.14.2 in /opt/conda/lib/python3.7/site-packages (from imgaug) (0.19.3)\nRequirement already satisfied: Pillow in /opt/conda/lib/python3.7/site-packages (from imgaug) (9.4.0)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from imgaug) (3.5.3)\nRequirement already satisfied: Shapely in /opt/conda/lib/python3.7/site-packages (from imgaug) (1.8.0)\nRequirement already satisfied: opencv-python in /opt/conda/lib/python3.7/site-packages (from imgaug) (4.5.4.60)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from imgaug) (1.7.3)\nRequirement already satisfied: imageio in /opt/conda/lib/python3.7/site-packages (from imgaug) (2.25.0)\nRequirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from imgaug) (1.21.6)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from imgaug) (1.16.0)\nRequirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.7/site-packages (from scikit-image>=0.14.2->imgaug) (2.6.3)\nRequirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image>=0.14.2->imgaug) (1.3.0)\nRequirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.7/site-packages (from scikit-image>=0.14.2->imgaug) (2021.11.2)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image>=0.14.2->imgaug) (23.0)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->imgaug) (3.0.9)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->imgaug) (0.11.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->imgaug) (1.4.4)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->imgaug) (2.8.2)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->imgaug) (4.38.0)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->imgaug) (4.4.0)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m--2023-05-10 13:23:51--  https://github.com/DanielLin94144/ML-attack-dataset/files/8167812/data.zip\nResolving github.com (github.com)... 192.30.255.112\nConnecting to github.com (github.com)|192.30.255.112|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://objects.githubusercontent.com/github-production-repository-file-5c1aeb/465178219/8167812?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230510%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230510T132351Z&X-Amz-Expires=300&X-Amz-Signature=2e7badd9c050c367add25cf6f90c1b68c47f2248fd463408ba029c15451dbbd5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=465178219&response-content-disposition=attachment%3Bfilename%3Ddata.zip&response-content-type=application%2Fzip [following]\n--2023-05-10 13:23:52--  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[489509/489509]\n\nArchive:  ./data.zip\n   creating: data/\n   creating: data/deer/\n extracting: data/deer/deer13.png    \n extracting: data/deer/deer6.png     \n extracting: data/deer/deer11.png    \n extracting: data/deer/deer2.png     \n extracting: data/deer/deer10.png    \n extracting: data/deer/deer16.png    \n extracting: data/deer/deer9.png     \n extracting: data/deer/deer20.png    \n extracting: data/deer/deer15.png    \n extracting: data/deer/deer19.png    \n extracting: data/deer/deer5.png     \n extracting: data/deer/deer14.png    \n extracting: data/deer/deer4.png     \n extracting: data/deer/deer8.png     \n extracting: data/deer/deer12.png    \n extracting: data/deer/deer1.png     \n extracting: data/deer/deer7.png     \n extracting: data/deer/deer17.png    \n extracting: data/deer/deer18.png    \n extracting: data/deer/deer3.png     \n   creating: data/horse/\n extracting: data/horse/horse9.png   \n extracting: data/horse/horse1.png   \n extracting: data/horse/horse16.png  \n extracting: data/horse/horse15.png  \n extracting: data/horse/horse19.png  \n extracting: data/horse/horse14.png  \n extracting: data/horse/horse10.png  \n extracting: data/horse/horse7.png   \n extracting: data/horse/horse2.png   \n extracting: data/horse/horse6.png   \n extracting: data/horse/horse20.png  \n extracting: data/horse/horse5.png   \n extracting: data/horse/horse18.png  \n extracting: data/horse/horse12.png  \n extracting: data/horse/horse13.png  \n extracting: data/horse/horse17.png  \n extracting: data/horse/horse4.png   \n extracting: data/horse/horse11.png  \n extracting: data/horse/horse8.png   \n extracting: data/horse/horse3.png   \n   creating: data/ship/\n extracting: data/ship/ship10.png    \n extracting: data/ship/ship14.png    \n extracting: data/ship/ship9.png     \n extracting: data/ship/ship20.png    \n extracting: data/ship/ship5.png     \n extracting: data/ship/ship8.png     \n extracting: data/ship/ship19.png    \n extracting: data/ship/ship16.png    \n extracting: data/ship/ship13.png    \n extracting: data/ship/ship6.png     \n extracting: data/ship/ship17.png    \n extracting: data/ship/ship1.png     \n extracting: data/ship/ship12.png    \n extracting: data/ship/ship2.png     \n extracting: data/ship/ship3.png     \n extracting: data/ship/ship15.png    \n extracting: data/ship/ship4.png     \n extracting: data/ship/ship7.png     \n extracting: data/ship/ship11.png    \n extracting: data/ship/ship18.png    \n   creating: data/frog/\n extracting: data/frog/frog10.png    \n extracting: data/frog/frog4.png     \n extracting: data/frog/frog5.png     \n extracting: data/frog/frog20.png    \n extracting: data/frog/frog15.png    \n extracting: data/frog/frog3.png     \n extracting: data/frog/frog1.png     \n extracting: data/frog/frog14.png    \n extracting: data/frog/frog2.png     \n extracting: data/frog/frog19.png    \n extracting: data/frog/frog7.png     \n extracting: data/frog/frog11.png    \n extracting: data/frog/frog17.png    \n extracting: data/frog/frog18.png    \n extracting: data/frog/frog12.png    \n extracting: data/frog/frog16.png    \n extracting: data/frog/frog8.png     \n extracting: data/frog/frog13.png    \n extracting: data/frog/frog6.png     \n extracting: data/frog/frog9.png     \n   creating: data/airplane/\n extracting: data/airplane/airplane3.png  \n extracting: data/airplane/airplane4.png  \n extracting: data/airplane/airplane2.png  \n extracting: data/airplane/airplane9.png  \n extracting: data/airplane/airplane20.png  \n extracting: data/airplane/airplane18.png  \n extracting: data/airplane/airplane19.png  \n extracting: data/airplane/airplane10.png  \n extracting: data/airplane/airplane6.png  \n extracting: data/airplane/airplane13.png  \n extracting: data/airplane/airplane16.png  \n extracting: data/airplane/airplane14.png  \n extracting: data/airplane/airplane11.png  \n extracting: data/airplane/airplane1.png  \n extracting: data/airplane/airplane17.png  \n extracting: data/airplane/airplane7.png  \n extracting: data/airplane/airplane15.png  \n extracting: data/airplane/airplane5.png  \n extracting: data/airplane/airplane8.png  \n extracting: data/airplane/airplane12.png  \n   creating: data/bird/\n extracting: data/bird/bird9.png     \n extracting: data/bird/bird12.png    \n extracting: data/bird/bird10.png    \n extracting: data/bird/bird11.png    \n extracting: data/bird/bird5.png     \n extracting: data/bird/bird8.png     \n extracting: data/bird/bird4.png     \n extracting: data/bird/bird3.png     \n extracting: data/bird/bird7.png     \n extracting: data/bird/bird18.png    \n extracting: data/bird/bird14.png    \n extracting: data/bird/bird13.png    \n extracting: data/bird/bird2.png     \n extracting: data/bird/bird15.png    \n extracting: data/bird/bird17.png    \n extracting: data/bird/bird19.png    \n extracting: data/bird/bird16.png    \n extracting: data/bird/bird6.png     \n extracting: data/bird/bird20.png    \n extracting: data/bird/bird1.png     \n   creating: data/cat/\n extracting: data/cat/cat6.png       \n extracting: data/cat/cat1.png       \n extracting: data/cat/cat7.png       \n extracting: data/cat/cat19.png      \n extracting: data/cat/cat5.png       \n extracting: data/cat/cat9.png       \n extracting: data/cat/cat17.png      \n extracting: data/cat/cat2.png       \n extracting: data/cat/cat16.png      \n extracting: data/cat/cat10.png      \n extracting: data/cat/cat4.png       \n extracting: data/cat/cat18.png      \n extracting: data/cat/cat13.png      \n extracting: data/cat/cat11.png      \n extracting: data/cat/cat20.png      \n extracting: data/cat/cat15.png      \n extracting: data/cat/cat8.png       \n extracting: data/cat/cat14.png      \n extracting: data/cat/cat3.png       \n extracting: data/cat/cat12.png      \n   creating: data/automobile/\n extracting: data/automobile/automobile17.png  \n extracting: data/automobile/automobile11.png  \n extracting: data/automobile/automobile5.png  \n extracting: data/automobile/automobile10.png  \n extracting: data/automobile/automobile20.png  \n extracting: data/automobile/automobile2.png  \n extracting: data/automobile/automobile6.png  \n extracting: data/automobile/automobile1.png  \n extracting: data/automobile/automobile19.png  \n extracting: data/automobile/automobile7.png  \n extracting: data/automobile/automobile16.png  \n extracting: data/automobile/automobile3.png  \n extracting: data/automobile/automobile14.png  \n extracting: data/automobile/automobile12.png  \n extracting: data/automobile/automobile9.png  \n extracting: data/automobile/automobile4.png  \n extracting: data/automobile/automobile8.png  \n extracting: data/automobile/automobile13.png  \n extracting: data/automobile/automobile18.png  \n extracting: data/automobile/automobile15.png  \n   creating: data/dog/\n extracting: data/dog/dog9.png       \n extracting: data/dog/dog2.png       \n extracting: data/dog/dog15.png      \n extracting: data/dog/dog8.png       \n extracting: data/dog/dog3.png       \n extracting: data/dog/dog19.png      \n extracting: data/dog/dog12.png      \n extracting: data/dog/dog7.png       \n extracting: data/dog/dog17.png      \n extracting: data/dog/dog11.png      \n extracting: data/dog/dog16.png      \n extracting: data/dog/dog20.png      \n extracting: data/dog/dog4.png       \n extracting: data/dog/dog5.png       \n extracting: data/dog/dog14.png      \n extracting: data/dog/dog18.png      \n extracting: data/dog/dog10.png      \n extracting: data/dog/dog1.png       \n extracting: data/dog/dog13.png      \n extracting: data/dog/dog6.png       \n   creating: data/truck/\n extracting: data/truck/truck1.png   \n extracting: data/truck/truck18.png  \n extracting: data/truck/truck9.png   \n extracting: data/truck/truck4.png   \n extracting: data/truck/truck14.png  \n extracting: data/truck/truck8.png   \n extracting: data/truck/truck12.png  \n extracting: data/truck/truck15.png  \n extracting: data/truck/truck2.png   \n extracting: data/truck/truck5.png   \n extracting: data/truck/truck3.png   \n extracting: data/truck/truck10.png  \n extracting: data/truck/truck17.png  \n extracting: data/truck/truck20.png  \n extracting: data/truck/truck19.png  \n extracting: data/truck/truck13.png  \n extracting: data/truck/truck7.png   \n extracting: data/truck/truck6.png   \n  inflating: data/truck/truck16.png  \n extracting: data/truck/truck11.png  \n","output_type":"stream"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nbatch_size = 8","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:23:54.960063Z","iopub.execute_input":"2023-05-10T13:23:54.960762Z","iopub.status.idle":"2023-05-10T13:23:57.308582Z","shell.execute_reply.started":"2023-05-10T13:23:54.960713Z","shell.execute_reply":"2023-05-10T13:23:57.307446Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"## 全局设置\n#### **[NOTE]**: 不要更改此处的设置，否则您生成的图像可能不符合限制\n* $\\epsilon$ 设定为固定值 8. 但是在 **Data section**, 我们将首先对原始像素值应用变换 (0-255)范围 **转换成 (0-1)范围** 然后 **标准化 (减去mean除以std)**. 所以$\\epsilon$ 在攻击期间应该设置为 $\\frac{8}{255 * std}$ 。\n\n* 解释 (optional)\n    * 将原始图像的第一个像素表示为$p$，将对抗性图像的第一像素表示为$a$。\n    * $\\epsilon$ 约束告诉我们 $\\left| p-a \\right| <= 8$.\n    * ToTensor()函数为 $T(x) = x/255$\n    * Normalize()函数为 $N(x) = (x-mean)/std$， $mean$ 和 $std$ 是常数\n    * 对 $p$ 和 $a$ 进行ToTensor()和 Normalize() 后 , 约束变成 $\\left| N(T(p))-N(T(a)) \\right| = \\left| \\frac{\\frac{p}{255}-mean}{std}-\\frac{\\frac{a}{255}-mean}{std} \\right| = \\frac{1}{255 * std} \\left| p-a \\right| <= \\frac{8}{255 * std}.$\n    * 所以，在经过 ToTensor() 和 Normalize()之后，我们需要设置 $\\epsilon$ 为 $\\frac{8}{255 * std}$ .","metadata":{}},{"cell_type":"code","source":"# 平均值和标准差是根据cifar10数据集计算的统计数据\ncifar_10_mean = (0.491, 0.482, 0.447) # cifar_10 图片数据三个通道的均值\ncifar_10_std = (0.202, 0.199, 0.201) # cifar_10 图片数据三个通道的标准差\n\n# 将mean和std转换为三维张量，用于未来的运算\nmean = torch.tensor(cifar_10_mean).to(device).view(3, 1, 1)\nstd = torch.tensor(cifar_10_std).to(device).view(3, 1, 1)\n\nepsilon = 8/255/std","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:23:57.310402Z","iopub.execute_input":"2023-05-10T13:23:57.311075Z","iopub.status.idle":"2023-05-10T13:23:59.949719Z","shell.execute_reply.started":"2023-05-10T13:23:57.311032Z","shell.execute_reply":"2023-05-10T13:23:59.948301Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"root = './data' # 用于存储`benign images`的目录\n# benign images: 不包含对抗性扰动的图像\n# adversarial images: 包括对抗性扰动的图像","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:23:59.953043Z","iopub.execute_input":"2023-05-10T13:23:59.953447Z","iopub.status.idle":"2023-05-10T13:23:59.974348Z","shell.execute_reply.started":"2023-05-10T13:23:59.953407Z","shell.execute_reply":"2023-05-10T13:23:59.968258Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"## Data\n\n从根目录构建数据集和数据加载器。请注意，我们存储每个图像的文件名以备后续使用。","metadata":{}},{"cell_type":"code","source":"import os\nimport glob\nimport shutil\nimport numpy as np\nfrom PIL import Image\nfrom torchvision.transforms import transforms\nfrom torch.utils.data import Dataset, DataLoader\n\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(cifar_10_mean, cifar_10_std)\n])\n\nclass AdvDataset(Dataset):\n    def __init__(self, data_dir, transform):\n        self.images = []\n        self.labels = []\n        self.names = []\n        '''\n        data_dir\n        ├── class_dir\n        │   ├── class1.png\n        │   ├── ...\n        │   ├── class20.png\n        '''\n        for i, class_dir in enumerate(sorted(glob.glob(f'{data_dir}/*'))):\n            images = sorted(glob.glob(f'{class_dir}/*'))\n            self.images += images\n            self.labels += ([i] * len(images))\n            self.names += [os.path.relpath(imgs, data_dir) for imgs in images]\n        self.transform = transform\n    def __getitem__(self, idx):\n        image = self.transform(Image.open(self.images[idx]))\n        label = self.labels[idx]\n        return image, label\n    def __getname__(self):\n        return self.names\n    def __len__(self):\n        return len(self.images)\n\nadv_set = AdvDataset(root, transform=transform)\nadv_names = adv_set.__getname__()\nadv_loader = DataLoader(adv_set, batch_size=batch_size, shuffle=False)\n\nprint(f'number of images = {adv_set.__len__()}')","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:23:59.976137Z","iopub.execute_input":"2023-05-10T13:23:59.978914Z","iopub.status.idle":"2023-05-10T13:24:00.256339Z","shell.execute_reply.started":"2023-05-10T13:23:59.978852Z","shell.execute_reply":"2023-05-10T13:24:00.255122Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"number of images = 200\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## &#x2728; Utils -- `Benign Images`评估","metadata":{"execution":{"iopub.status.busy":"2023-04-15T14:06:28.831231Z","iopub.execute_input":"2023-04-15T14:06:28.832053Z","iopub.status.idle":"2023-04-15T14:06:28.837512Z","shell.execute_reply.started":"2023-04-15T14:06:28.832003Z","shell.execute_reply":"2023-04-15T14:06:28.836161Z"}}},{"cell_type":"code","source":"# 评估模型在良性图像上的性能\ndef epoch_benign(model, loader, loss_fn):\n    model.eval()\n    train_acc, train_loss = 0.0, 0.0\n    for x, y in loader:\n        x, y = x.to(device), y.to(device)\n        yp = model(x)\n        loss = loss_fn(yp, y)\n        train_acc += (yp.argmax(dim=1) == y).sum().item()\n        train_loss += loss.item() * x.shape[0]\n    return train_acc / len(loader.dataset), train_loss / len(loader.dataset)","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:24:00.257827Z","iopub.execute_input":"2023-05-10T13:24:00.258189Z","iopub.status.idle":"2023-05-10T13:24:00.267306Z","shell.execute_reply.started":"2023-05-10T13:24:00.258158Z","shell.execute_reply":"2023-05-10T13:24:00.264702Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"## &#x2728; Utils -- 攻击算法(`Attack Algorithm`)\n","metadata":{}},{"cell_type":"code","source":"def fgsm(model, x, y, loss_fn, epsilon=epsilon):\n    x_adv = x.detach().clone() # 用良性图片初始化 x_adv\n    x_adv.requires_grad = True # 需要获取 x_adv 的梯度\n    loss = loss_fn(model(x_adv), y) # 计算损失\n    loss.backward()  \n    # fgsm: 在x_adv上使用梯度上升来最大化损失\n    grad = x_adv.grad.detach()\n    x_adv = x_adv + epsilon * grad.sign()\n    return x_adv\n\n# 在“全局设置”部分中将alpha设置为步长 \n# alpha和num_iter可以自己决定设定成何值\nalpha = 0.8 / 255 / std\ndef ifgsm(model, x, y, loss_fn, epsilon=epsilon, alpha=alpha, num_iter=20):\n    x_adv = x\n    # num_iter 次迭代\n    for i in range(num_iter):\n        x_adv = fgsm(model, x_adv, y, loss_fn, alpha) # 用（ε=α）调用fgsm以获得新的x_adv\n        # x_adv = x_adv.detach().clone()\n        # x_adv.requires_grad = True  \n        # loss = loss_fn(model(x_adv), y)  \n        # loss.backward()  \n        # grad = x_adv.grad.detach()\n        # x_adv = x_adv + alpha * grad.sign()\n        x_adv = torch.max(torch.min(x_adv, x+epsilon), x-epsilon) # x_adv 裁剪到 [x-epsilon, x+epsilon]范围\n    return x_adv\n\ndef mifgsm(model, x, y, loss_fn, epsilon=epsilon, alpha=alpha, num_iter=20, decay=1.0):\n    x_adv = x\n    # 初始化 momentum tensor\n    momentum = torch.zeros_like(x).detach().to(device)\n    # num_iter 次迭代\n    for i in range(num_iter):\n        x_adv = x_adv.detach().clone()\n        x_adv.requires_grad = True  \n        loss = loss_fn(model(x_adv), y)  \n        loss.backward()  \n        # TODO: Momentum calculation\n        grad = x_adv.grad.detach() + (1 - decay) * momentum\n        momentum = grad\n        x_adv = x_adv + alpha * grad.sign()\n        x_adv = torch.max(torch.min(x_adv, x+epsilon), x-epsilon) # x_adv 裁剪到 [x-epsilon, x+epsilon]范围\n    return x_adv","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:24:00.269018Z","iopub.execute_input":"2023-05-10T13:24:00.270027Z","iopub.status.idle":"2023-05-10T13:24:00.303583Z","shell.execute_reply.started":"2023-05-10T13:24:00.269997Z","shell.execute_reply":"2023-05-10T13:24:00.302609Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"## &#x2728; Utils -- Attack\n* 召回(Recall)\n  * ToTensor()函数为 $T(x) = x/255$\n  * Normalize()函数为 $N(x) = (x-mean)/std$， $mean$ 和 $std$ 是常数\n\n* 反函数(Inverse function)\n  * 反 Normalize() 函数为  $N^{-1}(x) = x*std+mean$ ，$mean$ 和 $std$ 是常数\n  * 反 ToTensor()  函数为  $T^{-1}(x) = x*255$.\n\n* **特别注意事项**\n  * ToTensor() 同时也会变换图片的shape `(height, width, channel)` -> `(channel, height, width)`, 所以我们还需要将形状转换回原始形状。\n  * 由于我们的数据加载器对一批数据进行采样，因此我们需要的是转置 **`(batch_size, channel, height, width)`** 变回 **`(batch_size, height, width, channel)`** 使用`np.transpose`.","metadata":{}},{"cell_type":"code","source":"# 执行对抗性攻击 并 生成对抗性示例\ndef gen_adv_examples(model, loader, attack, loss_fn):\n    model.eval()\n    adv_names = []\n    train_acc, train_loss = 0.0, 0.0\n    for i, (x, y) in enumerate(loader):\n        x, y = x.to(device), y.to(device)\n        x_adv = attack(model, x, y, loss_fn) # 获得对抗性示例\n        yp = model(x_adv)\n        loss = loss_fn(yp, y)\n        train_acc += (yp.argmax(dim=1) == y).sum().item()\n        train_loss += loss.item() * x.shape[0]\n        # 保存对抗性示例\n        adv_ex = ((x_adv) * std + mean).clamp(0, 1) # to 0-1 scale\n        adv_ex = (adv_ex * 255).clamp(0, 255) # 0-255 scale\n        adv_ex = adv_ex.detach().cpu().data.numpy().round() # round to remove decimal part\n        adv_ex = adv_ex.transpose((0, 2, 3, 1)) # transpose (bs, C, H, W) back to (bs, H, W, C)\n        adv_examples = adv_ex if i == 0 else np.r_[adv_examples, adv_ex]\n    return adv_examples, train_acc / len(loader.dataset), train_loss / len(loader.dataset)\n\n# 创建存储对抗性示例的目录\ndef create_dir(data_dir, adv_dir, adv_examples, adv_names):\n    if os.path.exists(adv_dir) is not True:\n        _ = shutil.copytree(data_dir, adv_dir)\n    for example, name in zip(adv_examples, adv_names):\n        im = Image.fromarray(example.astype(np.uint8)) # 图片数据需要转成 uint8\n        im.save(os.path.join(adv_dir, name))","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:24:00.305145Z","iopub.execute_input":"2023-05-10T13:24:00.305532Z","iopub.status.idle":"2023-05-10T13:24:00.319087Z","shell.execute_reply.started":"2023-05-10T13:24:00.305496Z","shell.execute_reply":"2023-05-10T13:24:00.317897Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"## Model / Loss Function\n\n可用模型列表 [here](https://github.com/osmr/imgclsmob/blob/master/pytorch/pytorchcv/model_provider.py). \n请选择后缀为_cifar10的模型。无法访问/加载某些模型，可以直接跳过，因为TA的模型不会使用这些类型的模型。","metadata":{}},{"cell_type":"code","source":"from pytorchcv.model_provider import get_model as ptcv_get_model\n\nmodel = ptcv_get_model('resnet110_cifar10', pretrained=True).to(device)\nloss_fn = nn.CrossEntropyLoss()\n\nbenign_acc, benign_loss = epoch_benign(model, adv_loader, loss_fn)\nprint(f'[ Base(未Attack图片评估) ] benign_acc = {benign_acc:.5f}, benign_loss = {benign_loss:.5f}')","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:24:00.320469Z","iopub.execute_input":"2023-05-10T13:24:00.321357Z","iopub.status.idle":"2023-05-10T13:24:05.620713Z","shell.execute_reply.started":"2023-05-10T13:24:00.321319Z","shell.execute_reply":"2023-05-10T13:24:05.617307Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Downloading /root/.torch/models/resnet110_cifar10-0369-4d6ca1fc.pth.zip from https://github.com/osmr/imgclsmob/releases/download/v0.0.163/resnet110_cifar10-0369-4d6ca1fc.pth.zip...\n[ Base(未Attack图片评估) ] benign_acc = 0.95000, benign_loss = 0.22678\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## FGSM","metadata":{}},{"cell_type":"code","source":"adv_examples, fgsm_acc, fgsm_loss = gen_adv_examples(model, adv_loader, fgsm, loss_fn)\nprint(f'[ Attack(FGSM Attack图片评估) ] fgsm_acc = {fgsm_acc:.5f}, fgsm_loss = {fgsm_loss:.5f}')\n\ncreate_dir(root, 'fgsm', adv_examples, adv_names)","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:24:05.628334Z","iopub.execute_input":"2023-05-10T13:24:05.629798Z","iopub.status.idle":"2023-05-10T13:24:08.110576Z","shell.execute_reply.started":"2023-05-10T13:24:05.629750Z","shell.execute_reply":"2023-05-10T13:24:08.109301Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"[ Attack(FGSM Attack图片评估) ] fgsm_acc = 0.59000, fgsm_loss = 2.49187\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## I-FGSM","metadata":{}},{"cell_type":"code","source":"adv_examples, ifgsm_acc, ifgsm_loss = gen_adv_examples(model, adv_loader, ifgsm, loss_fn)\nprint(f'[ Attack(I-FGSM Attack图片评估) ] ifgsm_acc = {ifgsm_acc:.5f}, ifgsm_loss = {ifgsm_loss:.5f}')\n\ncreate_dir(root, 'ifgsm', adv_examples, adv_names)","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:24:08.112348Z","iopub.execute_input":"2023-05-10T13:24:08.112727Z","iopub.status.idle":"2023-05-10T13:24:29.135043Z","shell.execute_reply.started":"2023-05-10T13:24:08.112689Z","shell.execute_reply":"2023-05-10T13:24:29.133283Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"[ Attack(I-FGSM Attack图片评估) ] ifgsm_acc = 0.01000, ifgsm_loss = 17.32269\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## 压缩打包图像\n* 上传压缩文件(·.tgz·)到 [JudgeBoi： https://ml.ee.ntu.edu.tw/hw10/](https://ml.ee.ntu.edu.tw/hw10/)","metadata":{}},{"cell_type":"code","source":"%cd fgsm\n!tar zcvf ../fgsm.tgz *\n%cd ..\n\n%cd ifgsm\n!tar zcvf ../ifgsm.tgz *\n%cd ..","metadata":{"execution":{"iopub.status.busy":"2023-05-08T14:28:52.632033Z","iopub.execute_input":"2023-05-08T14:28:52.632451Z","iopub.status.idle":"2023-05-08T14:28:54.756068Z","shell.execute_reply.started":"2023-05-08T14:28:52.632411Z","shell.execute_reply":"2023-05-08T14:28:54.754660Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"/kaggle/working/fgsm\nairplane/\nairplane/airplane10.png\nairplane/airplane2.png\nairplane/airplane8.png\nairplane/airplane13.png\nairplane/airplane16.png\nairplane/airplane4.png\nairplane/airplane17.png\nairplane/airplane15.png\nairplane/airplane19.png\nairplane/airplane11.png\nairplane/airplane7.png\nairplane/airplane6.png\nairplane/airplane3.png\nairplane/airplane5.png\nairplane/airplane20.png\nairplane/airplane18.png\nairplane/airplane12.png\nairplane/airplane9.png\nairplane/airplane14.png\nairplane/airplane1.png\nautomobile/\nautomobile/automobile13.png\nautomobile/automobile8.png\nautomobile/automobile19.png\nautomobile/automobile12.png\nautomobile/automobile17.png\nautomobile/automobile14.png\nautomobile/automobile20.png\nautomobile/automobile10.png\nautomobile/automobile15.png\nautomobile/automobile4.png\nautomobile/automobile2.png\nautomobile/automobile7.png\nautomobile/automobile6.png\nautomobile/automobile9.png\nautomobile/automobile18.png\nautomobile/automobile16.png\nautomobile/automobile11.png\nautomobile/automobile5.png\nautomobile/automobile3.png\nautomobile/automobile1.png\nbird/\nbird/bird1.png\nbird/bird17.png\nbird/bird6.png\nbird/bird11.png\nbird/bird18.png\nbird/bird19.png\nbird/bird2.png\nbird/bird10.png\nbird/bird5.png\nbird/bird3.png\nbird/bird14.png\nbird/bird16.png\nbird/bird15.png\nbird/bird20.png\nbird/bird8.png\nbird/bird13.png\nbird/bird12.png\nbird/bird7.png\nbird/bird9.png\nbird/bird4.png\ncat/\ncat/cat19.png\ncat/cat6.png\ncat/cat5.png\ncat/cat3.png\ncat/cat15.png\ncat/cat1.png\ncat/cat11.png\ncat/cat4.png\ncat/cat7.png\ncat/cat12.png\ncat/cat16.png\ncat/cat2.png\ncat/cat9.png\ncat/cat17.png\ncat/cat20.png\ncat/cat18.png\ncat/cat8.png\ncat/cat14.png\ncat/cat13.png\ncat/cat10.png\ndeer/\ndeer/deer4.png\ndeer/deer18.png\ndeer/deer2.png\ndeer/deer14.png\ndeer/deer20.png\ndeer/deer3.png\ndeer/deer19.png\ndeer/deer8.png\ndeer/deer16.png\ndeer/deer9.png\ndeer/deer5.png\ndeer/deer11.png\ndeer/deer15.png\ndeer/deer6.png\ndeer/deer17.png\ndeer/deer10.png\ndeer/deer7.png\ndeer/deer1.png\ndeer/deer12.png\ndeer/deer13.png\ndog/\ndog/dog20.png\ndog/dog19.png\ndog/dog4.png\ndog/dog3.png\ndog/dog9.png\ndog/dog15.png\ndog/dog6.png\ndog/dog5.png\ndog/dog14.png\ndog/dog1.png\ndog/dog17.png\ndog/dog13.png\ndog/dog10.png\ndog/dog8.png\ndog/dog16.png\ndog/dog18.png\ndog/dog11.png\ndog/dog7.png\ndog/dog2.png\ndog/dog12.png\nfrog/\nfrog/frog1.png\nfrog/frog11.png\nfrog/frog15.png\nfrog/frog4.png\nfrog/frog14.png\nfrog/frog12.png\nfrog/frog19.png\nfrog/frog8.png\nfrog/frog2.png\nfrog/frog5.png\nfrog/frog13.png\nfrog/frog3.png\nfrog/frog16.png\nfrog/frog9.png\nfrog/frog17.png\nfrog/frog20.png\nfrog/frog18.png\nfrog/frog7.png\nfrog/frog6.png\nfrog/frog10.png\nhorse/\nhorse/horse15.png\nhorse/horse5.png\nhorse/horse2.png\nhorse/horse8.png\nhorse/horse19.png\nhorse/horse10.png\nhorse/horse16.png\nhorse/horse6.png\nhorse/horse14.png\nhorse/horse17.png\nhorse/horse12.png\nhorse/horse9.png\nhorse/horse20.png\nhorse/horse11.png\nhorse/horse1.png\nhorse/horse13.png\nhorse/horse7.png\nhorse/horse3.png\nhorse/horse18.png\nhorse/horse4.png\nship/\nship/ship8.png\nship/ship13.png\nship/ship9.png\nship/ship19.png\nship/ship16.png\nship/ship14.png\nship/ship5.png\nship/ship10.png\nship/ship17.png\nship/ship20.png\nship/ship4.png\nship/ship15.png\nship/ship1.png\nship/ship7.png\nship/ship6.png\nship/ship18.png\nship/ship11.png\nship/ship12.png\nship/ship3.png\nship/ship2.png\ntruck/\ntruck/truck7.png\ntruck/truck5.png\ntruck/truck11.png\ntruck/truck9.png\ntruck/truck13.png\ntruck/truck8.png\ntruck/truck3.png\ntruck/truck2.png\ntruck/truck18.png\ntruck/truck4.png\ntruck/truck10.png\ntruck/truck17.png\ntruck/truck1.png\ntruck/truck16.png\ntruck/truck19.png\ntruck/truck14.png\ntruck/truck12.png\ntruck/truck15.png\ntruck/truck20.png\ntruck/truck6.png\n/kaggle/working\n/kaggle/working/ifgsm\nairplane/\nairplane/airplane10.png\nairplane/airplane2.png\nairplane/airplane8.png\nairplane/airplane13.png\nairplane/airplane16.png\nairplane/airplane4.png\nairplane/airplane17.png\nairplane/airplane15.png\nairplane/airplane19.png\nairplane/airplane11.png\nairplane/airplane7.png\nairplane/airplane6.png\nairplane/airplane3.png\nairplane/airplane5.png\nairplane/airplane20.png\nairplane/airplane18.png\nairplane/airplane12.png\nairplane/airplane9.png\nairplane/airplane14.png\nairplane/airplane1.png\nautomobile/\nautomobile/automobile13.png\nautomobile/automobile8.png\nautomobile/automobile19.png\nautomobile/automobile12.png\nautomobile/automobile17.png\nautomobile/automobile14.png\nautomobile/automobile20.png\nautomobile/automobile10.png\nautomobile/automobile15.png\nautomobile/automobile4.png\nautomobile/automobile2.png\nautomobile/automobile7.png\nautomobile/automobile6.png\nautomobile/automobile9.png\nautomobile/automobile18.png\nautomobile/automobile16.png\nautomobile/automobile11.png\nautomobile/automobile5.png\nautomobile/automobile3.png\nautomobile/automobile1.png\nbird/\nbird/bird1.png\nbird/bird17.png\nbird/bird6.png\nbird/bird11.png\nbird/bird18.png\nbird/bird19.png\nbird/bird2.png\nbird/bird10.png\nbird/bird5.png\nbird/bird3.png\nbird/bird14.png\nbird/bird16.png\nbird/bird15.png\nbird/bird20.png\nbird/bird8.png\nbird/bird13.png\nbird/bird12.png\nbird/bird7.png\nbird/bird9.png\nbird/bird4.png\ncat/\ncat/cat19.png\ncat/cat6.png\ncat/cat5.png\ncat/cat3.png\ncat/cat15.png\ncat/cat1.png\ncat/cat11.png\ncat/cat4.png\ncat/cat7.png\ncat/cat12.png\ncat/cat16.png\ncat/cat2.png\ncat/cat9.png\ncat/cat17.png\ncat/cat20.png\ncat/cat18.png\ncat/cat8.png\ncat/cat14.png\ncat/cat13.png\ncat/cat10.png\ndeer/\ndeer/deer4.png\ndeer/deer18.png\ndeer/deer2.png\ndeer/deer14.png\ndeer/deer20.png\ndeer/deer3.png\ndeer/deer19.png\ndeer/deer8.png\ndeer/deer16.png\ndeer/deer9.png\ndeer/deer5.png\ndeer/deer11.png\ndeer/deer15.png\ndeer/deer6.png\ndeer/deer17.png\ndeer/deer10.png\ndeer/deer7.png\ndeer/deer1.png\ndeer/deer12.png\ndeer/deer13.png\ndog/\ndog/dog20.png\ndog/dog19.png\ndog/dog4.png\ndog/dog3.png\ndog/dog9.png\ndog/dog15.png\ndog/dog6.png\ndog/dog5.png\ndog/dog14.png\ndog/dog1.png\ndog/dog17.png\ndog/dog13.png\ndog/dog10.png\ndog/dog8.png\ndog/dog16.png\ndog/dog18.png\ndog/dog11.png\ndog/dog7.png\ndog/dog2.png\ndog/dog12.png\nfrog/\nfrog/frog1.png\nfrog/frog11.png\nfrog/frog15.png\nfrog/frog4.png\nfrog/frog14.png\nfrog/frog12.png\nfrog/frog19.png\nfrog/frog8.png\nfrog/frog2.png\nfrog/frog5.png\nfrog/frog13.png\nfrog/frog3.png\nfrog/frog16.png\nfrog/frog9.png\nfrog/frog17.png\nfrog/frog20.png\nfrog/frog18.png\nfrog/frog7.png\nfrog/frog6.png\nfrog/frog10.png\nhorse/\nhorse/horse15.png\nhorse/horse5.png\nhorse/horse2.png\nhorse/horse8.png\nhorse/horse19.png\nhorse/horse10.png\nhorse/horse16.png\nhorse/horse6.png\nhorse/horse14.png\nhorse/horse17.png\nhorse/horse12.png\nhorse/horse9.png\nhorse/horse20.png\nhorse/horse11.png\nhorse/horse1.png\nhorse/horse13.png\nhorse/horse7.png\nhorse/horse3.png\nhorse/horse18.png\nhorse/horse4.png\nship/\nship/ship8.png\nship/ship13.png\nship/ship9.png\nship/ship19.png\nship/ship16.png\nship/ship14.png\nship/ship5.png\nship/ship10.png\nship/ship17.png\nship/ship20.png\nship/ship4.png\nship/ship15.png\nship/ship1.png\nship/ship7.png\nship/ship6.png\nship/ship18.png\nship/ship11.png\nship/ship12.png\nship/ship3.png\nship/ship2.png\ntruck/\ntruck/truck7.png\ntruck/truck5.png\ntruck/truck11.png\ntruck/truck9.png\ntruck/truck13.png\ntruck/truck8.png\ntruck/truck3.png\ntruck/truck2.png\ntruck/truck18.png\ntruck/truck4.png\ntruck/truck10.png\ntruck/truck17.png\ntruck/truck1.png\ntruck/truck16.png\ntruck/truck19.png\ntruck/truck14.png\ntruck/truck12.png\ntruck/truck15.png\ntruck/truck20.png\ntruck/truck6.png\n/kaggle/working\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## 集合攻击示例\n* 集成多个模型作为代理模型，以提高黑匣子的可转移性 ([paper](https://arxiv.org/abs/1611.02770))\n\n<font color=darkred><b>***TODO***: 将多模型预测结果（logits）累加 </font></b>\n","metadata":{}},{"cell_type":"code","source":"class ensembleNet(nn.Module):\n    def __init__(self, model_names):\n        super().__init__()\n        self.models = nn.ModuleList([ptcv_get_model(name, pretrained=True) for name in model_names])\n        self.softmax = nn.Softmax(dim=1)\n    def forward(self, x):\n        model\n        for i, m in enumerate(self.models):\n        # TODO: sum up logits from multiple models  \n            if i == 0:\n                res = m(x)\n                continue\n            res += m(x)\n        return self.softmax(res)","metadata":{"execution":{"iopub.status.busy":"2023-05-10T09:43:24.028289Z","iopub.execute_input":"2023-05-10T09:43:24.031972Z","iopub.status.idle":"2023-05-10T09:43:24.044006Z","shell.execute_reply.started":"2023-05-10T09:43:24.031915Z","shell.execute_reply":"2023-05-10T09:43:24.042802Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"model_names = [\n    'nin_cifar10',\n    'resnet20_cifar10',\n    'preresnet20_cifar10'\n]\nensemble_model = ensembleNet(model_names).to(device)\nensemble_model.eval()","metadata":{"execution":{"iopub.status.busy":"2023-05-10T09:43:26.244147Z","iopub.execute_input":"2023-05-10T09:43:26.244828Z","iopub.status.idle":"2023-05-10T09:43:27.763643Z","shell.execute_reply.started":"2023-05-10T09:43:26.244784Z","shell.execute_reply":"2023-05-10T09:43:27.762667Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Downloading /root/.torch/models/nin_cifar10-0743-795b0824.pth.zip from https://github.com/osmr/imgclsmob/releases/download/v0.0.175/nin_cifar10-0743-795b0824.pth.zip...\nDownloading /root/.torch/models/resnet20_cifar10-0597-9b0024ac.pth.zip from https://github.com/osmr/imgclsmob/releases/download/v0.0.163/resnet20_cifar10-0597-9b0024ac.pth.zip...\nDownloading /root/.torch/models/preresnet20_cifar10-0651-76cec68d.pth.zip from https://github.com/osmr/imgclsmob/releases/download/v0.0.164/preresnet20_cifar10-0651-76cec68d.pth.zip...\n","output_type":"stream"},{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"ensembleNet(\n  (models): ModuleList(\n    (0): CIFARNIN(\n      (features): Sequential(\n        (stage1): Sequential(\n          (unit1): NINConv(\n            (conv): Conv2d(3, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n            (activ): ReLU(inplace=True)\n          )\n          (unit2): NINConv(\n            (conv): Conv2d(192, 160, kernel_size=(1, 1), stride=(1, 1))\n            (activ): ReLU(inplace=True)\n          )\n          (unit3): NINConv(\n            (conv): Conv2d(160, 96, kernel_size=(1, 1), stride=(1, 1))\n            (activ): ReLU(inplace=True)\n          )\n        )\n        (stage2): Sequential(\n          (pool2): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n          (dropout2): Dropout(p=0.5, inplace=False)\n          (unit1): NINConv(\n            (conv): Conv2d(96, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n            (activ): ReLU(inplace=True)\n          )\n          (unit2): NINConv(\n            (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))\n            (activ): ReLU(inplace=True)\n          )\n          (unit3): NINConv(\n            (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))\n            (activ): ReLU(inplace=True)\n          )\n        )\n        (stage3): Sequential(\n          (pool3): AvgPool2d(kernel_size=3, stride=2, padding=1)\n          (dropout3): Dropout(p=0.5, inplace=False)\n          (unit1): NINConv(\n            (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (activ): ReLU(inplace=True)\n          )\n          (unit2): NINConv(\n            (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))\n            (activ): ReLU(inplace=True)\n          )\n        )\n      )\n      (output): Sequential(\n        (final_conv): NINConv(\n          (conv): Conv2d(192, 10, kernel_size=(1, 1), stride=(1, 1))\n          (activ): ReLU(inplace=True)\n        )\n        (final_pool): AvgPool2d(kernel_size=8, stride=1, padding=0)\n      )\n    )\n    (1): CIFARResNet(\n      (features): Sequential(\n        (init_block): ConvBlock(\n          (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n          (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activ): ReLU(inplace=True)\n        )\n        (stage1): Sequential(\n          (unit1): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n          (unit2): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n          (unit3): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n        )\n        (stage2): Sequential(\n          (unit1): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (identity_conv): ConvBlock(\n              (conv): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n              (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            )\n            (activ): ReLU(inplace=True)\n          )\n          (unit2): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n          (unit3): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n        )\n        (stage3): Sequential(\n          (unit1): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (identity_conv): ConvBlock(\n              (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n              (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            )\n            (activ): ReLU(inplace=True)\n          )\n          (unit2): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n          (unit3): ResUnit(\n            (body): ResBlock(\n              (conv1): ConvBlock(\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n              )\n              (conv2): ConvBlock(\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n              )\n            )\n            (activ): ReLU(inplace=True)\n          )\n        )\n        (final_pool): AvgPool2d(kernel_size=8, stride=1, padding=0)\n      )\n      (output): Linear(in_features=64, out_features=10, bias=True)\n    )\n    (2): CIFARPreResNet(\n      (features): Sequential(\n        (init_block): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        (stage1): Sequential(\n          (unit1): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n          (unit2): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n          (unit3): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n        )\n        (stage2): Sequential(\n          (unit1): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n            (identity_conv): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n          )\n          (unit2): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n          (unit3): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n        )\n        (stage3): Sequential(\n          (unit1): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n            (identity_conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n          )\n          (unit2): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n          (unit3): PreResUnit(\n            (body): PreResBlock(\n              (conv1): PreConvBlock(\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n              (conv2): PreConvBlock(\n                (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n                (activ): ReLU(inplace=True)\n                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n              )\n            )\n          )\n        )\n        (post_activ): PreResActivation(\n          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activ): ReLU(inplace=True)\n        )\n        (final_pool): AvgPool2d(kernel_size=8, stride=1, padding=0)\n      )\n      (output): Linear(in_features=64, out_features=10, bias=True)\n    )\n  )\n  (softmax): Softmax(dim=1)\n)"},"metadata":{}}]},{"cell_type":"markdown","source":"## Visualization","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nclasses = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n\nplt.figure(figsize=(10, 20))\ncnt = 0\nfor i, cls_name in enumerate(classes):\n    path = f'{cls_name}/{cls_name}1.png'\n    # 未Attack图片（benign image）\n    cnt += 1\n    plt.subplot(len(classes), 4, cnt)\n    im = Image.open(f'./data/{path}')\n    logit = model(transform(im).unsqueeze(0).to(device))[0]\n    predict = logit.argmax(-1).item()\n    prob = logit.softmax(-1)[predict].item()\n    plt.title(f'benign: {cls_name}1.png\\n{classes[predict]}: {prob:.2%}')\n    plt.axis('off')\n    plt.imshow(np.array(im))\n    # Attack后图片（adversarial image）\n    cnt += 1\n    plt.subplot(len(classes), 4, cnt)\n    im = Image.open(f'./fgsm/{path}')\n    logit = model(transform(im).unsqueeze(0).to(device))[0]\n    predict = logit.argmax(-1).item()\n    prob = logit.softmax(-1)[predict].item()\n    plt.title(f'adversarial: {cls_name}1.png\\n{classes[predict]}: {prob:.2%}')\n    plt.axis('off')\n    plt.imshow(np.array(im))\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:26:14.681859Z","iopub.execute_input":"2023-05-10T13:26:14.682397Z","iopub.status.idle":"2023-05-10T13:26:16.375608Z","shell.execute_reply.started":"2023-05-10T13:26:14.682342Z","shell.execute_reply":"2023-05-10T13:26:16.374631Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x2000 with 20 Axes>","image/png":"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报告问题\n* 请确保您遵循以下设置：源模型是`resnet110_cifar10`，对“dog2.png”应用vanilla fgsm攻击。您可以在“fgsm/dog2.png'”中找到受干扰的图像。","metadata":{}},{"cell_type":"code","source":"# original image\npath = f'dog/dog2.png'\nim = Image.open(f'./data/{path}')\nlogit = model(transform(im).unsqueeze(0).to(device))[0]\npredict = logit.argmax(-1).item()\nprob = logit.softmax(-1)[predict].item()\nplt.title(f'benign: dog2.png\\n{classes[predict]}: {prob:.2%}')\nplt.axis('off')\nplt.imshow(np.array(im))\nplt.tight_layout()\nplt.show()\n\n# adversarial image \nim = Image.open(f'./fgsm/{path}')\nlogit = model(transform(im).unsqueeze(0).to(device))[0]\npredict = logit.argmax(-1).item()\nprob = logit.softmax(-1)[predict].item()\nplt.title(f'adversarial: dog2.png\\n{classes[predict]}: {prob:.2%}')\nplt.axis('off')\nplt.imshow(np.array(im))\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:28:51.567126Z","iopub.execute_input":"2023-05-10T13:28:51.567557Z","iopub.status.idle":"2023-05-10T13:28:51.855660Z","shell.execute_reply.started":"2023-05-10T13:28:51.567523Z","shell.execute_reply":"2023-05-10T13:28:51.854305Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"## &#x2728; 被动防御(`Passive Defense`)-JPEG压缩\n通过imgaug包进行JPEG压缩，压缩率设置为70\n\n参考: https://imgaug.readthedocs.io/en/latest/source/api_augmenters_arithmetic.html#imgaug.augmenters.arithmetic.JpegCompression","metadata":{}},{"cell_type":"code","source":"import imgaug.augmenters as iaa\n\n# 预处理image\nx = transforms.ToTensor()(im)*255\nx = x.permute(1, 2, 0).numpy()\ncompressed_x = x.astype(np.uint8)\n\n\nlogit = model(transform(compressed_x).unsqueeze(0).to(device))[0]\npredict = logit.argmax(-1).item()\nprob = logit.softmax(-1)[predict].item()\nplt.title(f'JPEG adversarial: dog2.png\\n{classes[predict]}: {prob:.2%}')\nplt.axis('off')\nplt.imshow(compressed_x)\nplt.tight_layout()\nplt.show()\n\n\n# TODO: use \"imgaug\" package to perform JPEG compression (compression rate = 70)\ncmp_model = iaa.arithmetic.JpegCompression(compression=70)\ncompressed_x = cmp_model(images=compressed_x)\n\nlogit = model(transform(compressed_x).unsqueeze(0).to(device))[0]\npredict = logit.argmax(-1).item()\nprob = logit.softmax(-1)[predict].item()\nplt.title(f'JPEG assive Defense: dog2.png\\n{classes[predict]}: {prob:.2%}')\nplt.axis('off')\nplt.imshow(compressed_x)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-10T13:29:01.715018Z","iopub.execute_input":"2023-05-10T13:29:01.715991Z","iopub.status.idle":"2023-05-10T13:29:03.329194Z","shell.execute_reply.started":"2023-05-10T13:29:01.715950Z","shell.execute_reply":"2023-05-10T13:29:03.327710Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/imgaug/augmenters/base.py:38: SuspiciousMultiImageShapeWarning: You provided a numpy array of shape (32, 32, 3) as a multi-image augmentation input, which was interpreted as (N, H, W). The last dimension however has value 1 or 3, which indicates that you provided a single image with shape (H, W, C) instead. If that is the case, you should use e.g. augmenter(image=<your input>) or augment_image(<your input>) -- note the singular 'image' instead of 'imageS'. Otherwise your single input image will be interpreted as multiple images of shape (H, W) during augmentation.\n  category=SuspiciousMultiImageShapeWarning)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}